Graft survival prediction in liver transplantation using artificial neural network models

The use of computer based learning models in medical domain has become a significant area of research. Organ transplantation is one of the main areas where prognosis models are being used for predicting the survival of patients. Post transplantation mortality rate is reduced if there exists an intelligent system that can pick out the correct donor-recipients pairs from a pool of donor and recipient data. In this paper, we propose a survival prediction model to define three month mortality of patients after Liver Transplantation. We used an Artificial Neural Network model for the survival rate of Liver Transplantation. The data for the study was gathered from United Network for Organ Sharing transplant registry. The main objective of the study is to develop a model for short-term survival prediction of liver patients. With 10-fold cross validation we were divided the whole data into training and test data which gives an accuracy of 99.74% by Multilayer Perceptron Artificial Neural Network model. We also compared the model with other classification models using various error performance measures. To ensure accuracy we experimented our model with existing models and proved the result. (C) 2016 Elsevier B.V. All rights reserved.